import os
import struct
import numpy as np

path  = r'E:\code\机器学习\RNN递归神经网络原理介绍+数字识别应用\MNIST_data'
def load_mnist(path, kind='train'):
    """Load MNIST data from `path`"""
    labels_path = os.path.join(path,
                               '%s-labels.idx1-ubyte'
                               % kind
                               )
    images_path = os.path.join(path,
                               '%s-images.idx3-ubyte'
                               % kind)

    with open(labels_path, 'rb') as lbpath:
        magic, n = struct.unpack('>II',
                                 lbpath.read(8))
        labels = np.fromfile(lbpath,
                             dtype=np.uint8)

    with open(images_path, 'rb') as imgpath:
        magic, num, rows, cols = struct.unpack('>IIII',
                                               imgpath.read(16))
        images = np.fromfile(imgpath,
                             dtype=np.uint8).reshape(len(labels), 784)
    # print(labels)
    return images, labels


import matplotlib.pyplot as plt
fig, ax = plt.subplots(
    nrows=2,
    ncols=5,
    sharex=True,
    sharey=True, )

ax = ax.flatten()
for i in range(10):
    img = X_train[y_train == i][0].reshape(28, 28)
    ax[i].imshow(img, cmap='Greys', interpolation='nearest')

ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()